Array assays between surface bound binding agents or probes and target molecules in solution are used to detect the presence of particular biopolymers. The surface-bound probes may be oligonucleotides, peptides, polypeptides, proteins, antibodies or other molecules capable of binding with target molecules in solution. Such binding interactions are the basis for many of the methods and devices used in a variety of different fields, e.g., genomics (in sequencing by hybridization, SNP detection, differential gene expression analysis, comparative genomic hybridization, identification of novel genes, gene mapping, finger printing, etc.) and proteomics.
One typical array assay method involves biopolymeric probes immobilized in an array on a substrate such as a glass substrate or the like. A solution containing analytes that bind with the attached probes is placed in contact with the array substrate, covered with another substrate such as a coverslip or the like to form an assay area and placed in an environmentally controlled chamber such as an incubator or the like. Usually, the targets in the solution bind to the complementary probes on the substrate to form a binding complex. The pattern of binding by target molecules to biopolymer probe features or spots on the substrate produces a pattern on the surface of the substrate and provides desired information about the sample. In most instances, the target molecules are labeled with a detectable tag such as a fluorescent tag or chemiluminescent tag. The resultant binding interaction or complexes of binding pairs are then detected and read or interrogated, for example by optical means, although other methods may also be used. For example, laser light may be used to excite fluorescent tags, generating a signal only in those spots on the biochip (substrate) that have a target molecule and thus a fluorescent tag bound to a probe molecule. This pattern may then be digitally scanned for computer analysis.
As such, optical scanners play an important role in many array based applications. Optical scanners act like a large field fluorescence microscope in which the fluorescent pattern caused by binding of labeled molecules on the array surface is scanned. In this way, a laser induced fluorescence scanner provides for analyzing large numbers of different target molecules of interest, e.g., genes/mutations/alleles, in a biological sample.
Scanning equipment used for the evaluation of arrays typically includes a scanning fluorometer. A number of different types of such devices are commercially available from different sources, such as Perkin-Elmer, Agilent Technologies, Inc., Axon Instruments, and others. In such devices, a laser light source generates a collimated beam. The collimated beam is focused on the array and sequentially illuminates small surface regions of known location on an array substrate. The resulting fluorescence signals from the surface regions are collected either confocally (employing the same lens to focus the laser light onto the array) or off-axis (using a separate lens positioned to one side of the lens used to focus the laser onto the array). The collected signals are then transmitted through appropriate spectral filters, to an optical detector. A recording device, such as a computer memory, records the detected signals and builds up a raster scan file of intensities as a function of position, or time as it relates to the position.
Analysis of the data (the stored file) may involve collection, reconstruction of the image, feature extraction from the image and quantification of the features extracted for use in comparison and interpretation of the data. Where large numbers of array files are to be analyzed, the various arrays from which the files were generated upon scanning may vary from each other with respect to a number of different characteristics, including the types of probes used (e.g., polypeptide or nucleic acid), the number of probes (features) deposited, the size, shape, density and position of the array of probes on the substrate, the geometry of the array, whether or not multiple arrays or subarrays are included on a single slide and thus in a single, stored file resultant from a scan of that slide, etc.
Previous automated solutions for feature extraction processing of images produced by scanning or one of the alternative techniques mentioned above do not have the capability of splitting image processing or feature extraction pre-processing and post-processing steps or procedures for analyzing the images. Thus, for processing images that contain multiple arrays or subarrays, for example, feature extraction processing, when using previous solutions, requires first splitting, cropping or dividing such images into component sub-images that each only contain a single array or subarray, prior to feature extracting the sub-images. This requires a user to save multiple files for a single image, one for each array or subarray.
Also, previous solutions typically do not have the ability to perform inter-array analysis, which refers to comparing post-processing results or outputs (i.e., processed outputs) of features located on different arrays. The different arrays on which the features are located may be on the same substrate or on different substrates. Inter-array analysis capabilities are important, particularly for one-color slides, substrates or arrays, or for finding a set of dye normalization probes from combined data analysis of multiple arrays, for example. Another important use for inter-array analysis is for performing a gene expression, proteomics, or other chemical or biochemical experiment where the content of the experiment extends over multiple arrays.
There remain continuing needs for improved solutions for efficiently imaging and analyzing array images in a manner where post processing of multiple arrays can be done in a combined fashion. Further, there are continuing needs for improved solutions for inter-array image processing while providing the ability to post-process separately.